Improved Genetic Programming for Symbolic Regression: Case Studies on Practical Applications
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Huynh:2022:SSCI,
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author = "Quang Huynh and Hemant Singh and Tapabrata Ray and
Akira Oyama",
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booktitle = "2022 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Improved Genetic Programming for Symbolic Regression:
Case Studies on Practical Applications",
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year = "2022",
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pages = "1135--1142",
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abstract = "Genetic Programming (GP), especially Semantic GP
(SGP), has shown significant potential in solving
numerical benchmarks in Symbolic Regression (SR) domain
in recent years. However, its application on real-world
problems has been less explored due to the large sizes
of the resulting expressions, which are prone to
over-fitting and are difficult to interpret. In this
paper, we propose a method that incorporates
customization for real-world data sets based on a
combination of two operators of GP. The first operator
uses the concept of Semantic Backpropagation, a
noteworthy method in SGP, to create short expressions
which are highly correlated with the outputs. The
second operator makes use of Mixed Integer Linear
Programming (MILP) to combine short expressions into
the overall expression with good accuracy. The proposed
approach is tested on one synthetic data set and two
practical applications which are challenging for
conventional GP. The experimental results are very
promising, with further scope of improvement.",
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keywords = "genetic algorithms, genetic programming,
Backpropagation, Sensitivity analysis, Semantics,
Benchmark testing, Mixed integer linear programming,
Computational intelligence",
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DOI = "doi:10.1109/SSCI51031.2022.10022279",
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month = dec,
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notes = "Also known as \cite{10022279}",
- }
Genetic Programming entries for
Quang Nhat Huynh
Hemant Singh
Tapabrata Ray
Akira Oyama
Citations